Advances in understanding multicellular organisms are encumbered because of inherent problems with cell and/or tissue heterogeneity. Particularly challenging, with regard to studies of cell type and/or tissue-specific gene expression, is the fact that cells and tissues of interest often cannot be isolated away from unwanted cells and tissues (White, Dunning et al. 1993)(Halgren, Fielden et al. 2001). Lay persons may observe that tissues and organs contain multiple different cell types not easily examined in isolation; for example, meat contains predominantly muscle but also contains interspersed nerves, connective tissue, tendon, blood, blood vessels, and fat. This heterogeneity is easily visible at the macroscopic level.
Microscopic examples of cellular heterogeneity include 1) nervous tissue, where in the presence of interspersed supporting cells such as glia, multiple different neuronal types interconnect to form a nervous system, and 2) skin, where each of the three layers contain multiple different cell types such as a) melanocytes and keratinocytes in the epidermal layer, b) macrophages, fibroblasts, and lymphocytes in the dermal layer, and c) endothelial cells and neurons in the hypodermal layer. Isolation or examination of individual cell types in heterogeneous tissue, and/or elucidating or ‘profiling’ expressed gene sequences from individual cell types, is presently difficult.
The inability to precisely isolate cell types and/or profile their molecular (i.e. nucleic acid) constituents from heterogeneous tissue can lead to incorrect assignment of gene expression, misinterpretation of gene function, and misinterpretation of functional elements controlling gene expression. This difficulty encumbers understanding multicellular organisms despite the availability of extensive genomic DNA sequence data, such as data obtained via the Human Genome Project.
A serious consequence of this ‘isolation’ or ‘profiling’ problem is the inability to isolate and compare differentially expressed genes within a heterogeneous tissue, that is, genes that are expressed at different levels, or not at all, between any two different cell-types within the tissue of interest. This difficulty encumbers identification of gene candidates in cells, tissues, organs, and whole organisms responsible or implicated in functions such as tissue growth, development, cell fate specification, cell death, organogenesis, and in aging. The problem exists in both normal and disease states. This difficulty encumbers development of reasonable hypotheses and the testing of precise molecular mechanisms in normal and diseased tissue, since putative genes thought to be involved are often unknown and/or poorly localized by standard methods.
A second serious consequence of this ‘isolation’ or ‘profiling’ problem is the inability to accurately compare levels of gene expression between tissues of varying complexity in an organism, that is, different tissues that contain widely different numbers of cell types. For example, if a gene appears to be expressed at a ‘high’ level in a simpler tissue such as liver, containing few putative cell types, as compared to a complex tissue such as brain tissue containing tens, hundreds, or possibly thousands of cell types, the comparison is virtually meaningless. One could postulate that a cell type present in complex (e.g. brain) tissue at a scant frequency expresses the same gene at a level equal to or many-fold greater than in the simpler (e.g. liver) tissue. This difficulty encumbers understanding environmental sensitivity to chemical compounds, and encumbers the appropriate choice of cellular targets for therapeutic intervention, for example, to reduce unwanted side effects.
A third serious consequence of this ‘isolation’ or ‘profiling’ problem is that it is presently difficult to determine if two cells express identical or near identical sets of genes, as would be expected for a given cell type. Because of the difficulty in determining which and how many cells belong to a particular cell type class by present empirical methods, two further difficulties arise: 1) it is difficult to accurately measure and express the similarity (or dissimilarity) of any two cell types in complex tissue, organs, or whole organisms, and 2) it is difficult to determine or estimate an absolute number of different cell types within complex tissue, organs, or whole organisms.
Cell types are commonly categorized on the basis of morphology, and/or cell surface antigens, and/or promoter activation. These empirical measures arguably fail to give a definitive answer as to whether or not the category of ‘positive’ cells actually constitute an individual cell type, since these measures often rely on the expression of a limited set of gene products, even as few as one gene product (e.g. a cell surface antigen).
A rigorous measure of whether two similar cells in a complex tissue actually constitute a single cell type would be a comparison of the sets of genes expressed in each cell. Different cells expressing identical and/or near identical sets of genes (e.g. represented as overlapping sets in a Venn diagram) can be thought of as constituting a single cell type. For complex solid tissue, rigorous measures for defining cell types, the number of cell types, and the relative similarity between different cell types would be highly desirable.
As an example, the nematode Caenorhabditis elegans has a nervous system as an adult animal of 302 neurons. These neurons have been categorized into 118 different cell types on morphological grounds (White 1986). It is presently unknown if these putative 118 cell types are simply cells that bear a superficial morphological resemblance to each other but are otherwise distinct. Two possibilities exist: in reality the number of cell types—as classified by cells expressing identical sets of genes—is actually much lower, possibly as few as a dozen different neuronal types. Alternatively as many as 302 different neuronal cell types may be present in C. elegans, that is, all neuronal cells are unique as defined by the criteria of expressed gene sets. This question can be extended to other complex tissues and/or organs in multi-cellular organisms, such as brain tissue, spinal cord, cardiac tissue, liver, kidney, etc.
The question of cell type number can even be extended to tissues that appear to be superficially simple, or to contain identical cells. For example, developing Drosophila embryos contain superficially identical cells at the cellular blastoderm stage. However, despite their superficial similarity, cells are already expressing genes involved in determining body plan in intricate and precise banding patterns. In Drosophila, these patterns are generated according to anterior-posterior and dorsal-ventral position. These data demonstrate mere morphological similarity between cells is no guarantee that they are expressing identical sets of genes. In fact, superficially similar cells may be responding to distal inductive signals.
The inability to easily assign cells to cell types, to determine a number of different cell types for a given tissue, and to accurately measure and express the similarity between different cell types within a specific tissue encumbers a) understanding and elucidating cellular roles in normal and diseased tissue during growth, development, and aging, b) understanding and elucidating environmental toxicology, c) appropriate choice of cellular targets for therapeutic intervention, and d) appropriate choice of cellular methods and reagents for therapeutic intervention (e.g. cell-based and/or tissue-based therapy).
For example, introduction of exogenous cells and or tissues (the basic technique adapted for use in therapy involving stem cells) would be critically and materially advanced as a therapeutic technique if exogenously-added cells and/or tissues could be reliably determined to be—or could be predictably induced to become—identical, similar, and/or compatible with endogenous cells and/or tissues.
The most common approach to cellular isolation in solid tissue is microdissection. Unfortunately, microdissection is often a difficult and error-prone technique. Microdissection also potentially allows for the disruption of normal gene expression patterns by the mechanical acts of cutting, crushing, and/or scraping, and its use may not result in data that accurately reflect in vivo gene expression patterns and levels. Thus novel isolation and/or profiling technique(s) are required precisely where microdissection is technically difficult or impossible, or may cause unforeseen changes in gene expression.
Cellular heterogeneity is a problem particularly evident in solid, complex tissues of the human body such as the brain, spinal cord, kidney, and in endocrine tissues such as the pituitary gland and pancreatic islet cells, etc. (Takeda, Yano et al. 1993; Chabardes-Garonne, Mejean et al. 2003; Kaestner, Lee et al. 2003; Cras-Meneur, Inoue et al. 2004), but is also relevant to solid tumors of the human body (Amatschek, Koenig et al. 2004), as well as in cells and tissues of model vertebrate organisms. Additionally, this problem is present in invertebrates and lower metazoans of biomedical, agricultural, and/or environmental interest, such as pathogenic and non-pathogenic nematodes, where a tissue or organ can consist of as few as tens or hundreds of cells (Andrews, Bouffard et al. 2000). As a consequence, the reliability and usefulness of modern techniques such as the use of DNA microarrays and serial analysis of gene expression (SAGE) is sharply limited.
Presently, the post-genomic era of biotechnology has made an organism's entire DNA sequence available to the biotechnology researcher, often on a chip. The implication for studying many model organisms is clear: gene function can be predicted by analogy to known genes, clones of genes are available, computer programs can rapidly predict locus elements such as promoters, enhancers, and splice sites, and genes and gene sequences can be compared to reference genomes or model organisms, etc.
Despite these advances, the era of post-genomic research has barely begun. Novel methodologies are required to advance an understanding of organisms, just as DNA sequencing advanced an understanding of genomes. Studies of complex interactions between cells in tissues, organs, and organisms promise to reveal a fascinating array of control mechanisms in the field of ‘tissue dynamics.’ Mechanisms include autocrine, paracrine, and endocrine control. The functional interplay of genes in different cell types will most likely be deciphered in well-studied model organisms. However the genomes of many organisms, including well-studied model organisms, remain un-interpretable despite the expenditure of resources to decipher their genetic content (e.g., cloning, mapping, and sequencing). This is because patterns of gene expression in complex tissue remain poorly understood. This experimental difficulty is present in both well-studied and recently introduced model organisms. For example, sequencing of the genome of the tunicate Ciona intestinalis has recently been completed, but the usefulness of this genomic information is limited because of insufficient knowledge of tissue-specific gene expression. Thus a great need exists for novel cell type-specific analytical techniques in advancing an understanding of the functional interplay between genes in most, if not all, metazoan organisms.
Currently, researchers often know an organism's genomic resources (e.g. genes), but not where and when the genes are expressed (i.e., in which tissues). Techniques are available that can be used to determine the expression pattern of individual genes (e.g., in situ hybridization), but this process is time-consuming and error-prone for the analysis of thousands, or tens of thousands, of genes. Furthermore, this analysis becomes even more burdensome when one considers that gene expression patterns are desired not only from normal tissues, but also from tissues subjected to various factors such as mutation, transformation, infection, and/or chemical (e.g., pharmacological) treatment.
For example, brain tissue contains a poorly understood cell type known as astrocytes, implicated in response to environmental insult (Sturrock 1988). Determining astrocyte-specific gene expression presents an enormous challenge, even in those organisms for which large amounts of brain tissue are readily available. Individual cell types are usually determined by histochemical and/or morphological methods. Cell type-specificity of individual genes, such as astrocyte-specific gene expression, is determined by serial techniques including in situ hybridization of the gene of interest and/or antibody detection of the gene product. For tissues that contain multiple different cell types (estimated tens to hundreds), it is presently difficult to isolate, examine, and/or profile the estimated hundreds or thousands of genes expressed in any particular cell type.
The actual physical isolation of specific cell types serves at least two different goals. The first is for the analysis of genetic material (DNA and RNA) from these cells, which is generally referred to as “molecular” analysis, that is, relating to molecular biology. The second is to examine cell growth in culture (cell culture) to investigate cellular responses, media requirements, autonomous/non-autonomous development, and expressed genes responsible for these characteristics. Once isolated, comparing gene expression in any two tissues of an organism is a valuable technique for determining gene function.
Paradoxically, it is presently easier to study the differential expression of genes in a mouse, a frog, or a human being than in some classically studied animals such flies and worms, the latter of which are related to parasitic animals that ravage the human population. Thus, a rapid method for elucidating (i.e. profiling) differential patterns of gene expression, otherwise known as a “molecular dissection” method, with or without cellular isolation, would be of great utility for the millions of species of poorly understood metazoan organisms.
Many of these poorly-understood metazoan organisms have profound biomedical, agricultural, and environmental relevance. For example, the World Health Organization estimates that two billion people worldwide—one-third of the world's population—are infected with worms such as Schistosoma and soil-transmitted helminths (STH). Two hundred million people are infected with Onchocerca volvulus, the cause of river blindness. Lymphatic filariasis and elephantiasis, which affect 120 million people worldwide, are caused by the related nematodes Brugia malayi and Wucheria bancrofti. 
Organisms of agricultural importance include the nematode Heterorhabdis bacteriophora, which is commercially available as a biocontrol agent (Riddle, Blumenthal et al. 1997). H. bacteriophora promiscuously parasitizes insect larvae. Haemonchus contortus, an intestinal parasite of sheep, is a serious agricultural pathogen. Non-pathogenic nematodes and related species have been proposed as organisms with potential for environmental toxicity testing and bioremediation (Williams and Dusenbery 1990; Donkin and Dusenbery 1993; Cressman and Williams 1997; Custodia, Won et al. 2001).
Developing new techniques for investigating tissue-specific gene expression is important for understanding multicellular organisms. Knowledge gained will allow gene pathways to be defined more rapidly, and will allow pharmacological targets to be selected with greater precision. Potential commercial products include, but are not limited to tissue-specific microarrays from model and parasitic organisms, cDNA libraries from specific cells and cell types, host determinant genes for pathogenic species, pharyngeal pumping genes for pharmacologic intervention, tissue-specific detoxifying genes induced in model and parasitic organism, and services to determine promoter activity in these metazoans. Ultimately these commercial tools will contribute to alleviating human suffering, increasing agricultural production, and improving the environment.
An important observation is that many of these poorly understood metazoan organisms of biomedical, agricultural, and environmental importance utilize an endogenous trans-splicing reaction in normal RNA processing. Other organisms may be induced to perform this trans-splicing reaction if no known reaction already exist. Thus cell isolation and/or cell-profiling techniques based on novel and inventive utilization of this reaction would have beneficial biomedical, agricultural, and environmental effects.
Thus, improved methods for use in identifying differential gene expression in cells and tissues that are not amenable to isolation represent a long-felt and ongoing need in the art. This and other needs are addressed by the presently disclosed subject matter.